Investigation of the effect of weather conditions on solar radiation in Brunei Darussalam

Yazdani, M. G.; Salam, Md. Abdus; Rahman, Quazi Mehbubar

doi:10.1080/14786451.2014.969266

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…From this, it is concluded that temperature and relative humidity are closely related to SR whereas wind speed has little influence over it. A study by Yazdani, et al 35 also displayed similar results as their study results show that SR is directly proportional to atmospheric temperature while inversely proportional to the relative humidity. It is also found that wind speed does not affect solar radiation as much as the other two meteorological parameters.…”

Section: Discussionsupporting

confidence: 73%

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Heng

Ridwan

Kumar

et al. 2022

Sci Rep

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Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg–Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.

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Section: Discussionsupporting

confidence: 73%

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Heng

Ridwan

Kumar

et al. 2022

Sci Rep

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“…Depending on the site, wind and solar variability are sometimes correlated and sometimes independent of each other (see, e.g., Ssekulima et al 7 ). For example, it has been found that wind speed has little influence on solar radiation at a specific site considered by Yazdani et al 8 On the other hand, solar irradiance and wind speed were found to be negatively correlated with the value of about

-0.4

in Britain by Bett and Thornton 9 …”

Section: Teaching Statistics and Probability With Renewable Energymentioning

confidence: 98%

Statistics and probability theory in renewable energy: Teaching and research

Eryılmaz

Kateri

Devrim

2023

Appl Stoch Models Bus & Ind

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In this paper, the key‐role and utility of statistics and probability theory in the field of renewable energy are emphasized and illustrated via specific examples. It is demonstrated that renewable energy is a very suitable field to effectively teach and implement many statistical and probabilistic concepts and techniques. From a research point of view, statistical and probabilistic methods have been successfully employed in evaluating renewable energy systems. These methods will continue to be of core interest for the renewable energy sector in the future, as new and more complex renewable energy systems are developed and installed. In this context, some future research directions in relation to the evaluation of renewable energy systems are also presented.

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“…ANNs have been widely used in research to solve complex problems. For example, Bayesian regularisation methods based on neural networks have been able to calculate physical parameters such as thermal relaxation parameter, Prandtl number, water suction/injection, and stretch/shrink paper, such as (Yazdani et al, 2016) and (Raja, Awan, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Solar Radiation Prediction: Case Study, Al-Qadisiyah, Iraq

Al-rubaye,

M. Al-Khuzaie

2024

Jou. Eng. Res.

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For solar energy to develop a clean, renewable alternative to fossil fuels, it is important to be able to correctly predict surface longwave radiation. To improve cost-efficiency and accuracy in surface longwave radiation (SLR) predictions, forecast systems are increasingly utilizing artificial neural networks (ANNs). This study uses two different procedures for predicting solar radiation in great detail. The first model uses weather statistics from a station in Al-Qadisiyah, Iraq. The second model, on the other hand, uses daily statistics from 2022 from NASA's Prediction of Worldwide Energy dataset for the same site. The scaled conjugate gradient technique was used in both models. The goal is to find the best mix of meteorological factors that can be used with an ANN model to achieve accurate predictions. Based on the findings of this study, temperature, relative humidity, and Rainfall all seem to have a big effect on SLR. On the other hand, windy weather doesn't have much of an effect on SLR. ANN models also did very well when trained with data from NASA's Prediction of Worldwide Energy, with an R2-value of 0.823 and an RMSE value of 0.0106. The results show that this mix does better than other models in terms of performance score.

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Investigation of the effect of weather conditions on solar radiation in Brunei Darussalam

Cited by 16 publications

References 7 publications

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Statistics and probability theory in renewable energy: Teaching and research

Artificial Neural Networks for Solar Radiation Prediction: Case Study, Al-Qadisiyah, Iraq

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